Study on strength properties and soil behaviour type classification of Huanghe River Delta silts based on variable rate piezocone penetration test

Fine-grained silt is widely distributed in the Huanghe River Delta(HRD)in China,and the sedimentary structure is complex,meaning that the clay content in the silt is variable.The piezocone penetration test(CPTu)is the most widely approved in situ test method.It can be used to invert soil properties and interpret soil behavior.To analyse the strength properties of surface sediments in the HRD,this paper evaluated the friction angle and its inversion formula through the CPTu penetration test and monotonic simple shear test and other soil unit experiments.The evaluation showed that the empirical formula proposed by Kulhawy and Mayne had better prediction and inversion effect.The HRD silts with clay contents of 9.2%,21.4%and 30.3%were selected as samples for the CPTu variable rate penetration test.The results show as follows.(1)The effects of the clay content on the tip resistance and the pore pressure of silt under different penetration rates were summarized.The tip resistance Q_t is strongly dependent on the clay content of the silt,the B_(q)value of the silt tends to 0 and is not significantly affected by the change of the CPTu penetration rate.(2)Five soil behavior type classification charts and three soil behavior type indexes based on CPTu data were evaluated.The results show that the soil behavior type classification chart based on soil behavior type index ISBT,the Robertson 2010 behavior type classification chart are more suitable for the silty soil in the HRD.

Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes.Much spatial information and spectral signatures of hyperspectral images(HSIs)present greater potential for detecting and classifying fine crops.The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging(RSI)has become an indispensable application in the agricultural domain.It is significant for the prediction and growth monitoring of crop yields.Amongst the deep learning(DL)techniques,Convolution Neural Network(CNN)was the best method for classifying HSI for their incredible local contextual modeling ability,enabling spectral and spatial feature extraction.This article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification(HMAODL-CTC)algorithm onHSI.The proposed HMAODL-CTC model mainly intends to categorize different types of crops on HSI.To accomplish this,the presented HMAODL-CTC model initially carries out image preprocessing to improve image quality.In addition,the presented HMAODL-CTC model develops dilated convolutional neural network(CNN)for feature extraction.For hyperparameter tuning of the dilated CNN model,the HMAO algorithm is utilized.Eventually,the presented HMAODL-CTC model uses an extreme learning machine(ELM)model for crop type classification.A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC algorithm.Extensive comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.

A Comprehensive Evaluation of State-of-the-Art Deep Learning Models for Road Surface Type Classification

In recent years,as intelligent transportation systems(ITS)such as autonomous driving and advanced driver-assistance systems have become more popular,there has been a rise in the need for different sources of traffic situation data.The classification of the road surface type,also known as the RST,is among the most essential of these situational data and can be utilized across the entirety of the ITS domain.Recently,the benefits of deep learning(DL)approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual methods.The ability to extract important features is vital in making RST classification more accurate.This work investigates the most recent advances in DL algorithms for sensor-based RST classification and explores appropriate feature extraction models.We used different convolutional neural networks to understand the functional architecture better;we constructed an enhanced DL model called SE-ResNet,which uses residual connections and squeeze-and-excitation mod-ules to improve the classification performance.Comparative experiments with a publicly available benchmark dataset,the passive vehicular sensors dataset,have shown that SE-ResNet outperforms other state-of-the-art models.The proposed model achieved the highest accuracy of 98.41%and the highest F1-score of 98.19%when classifying surfaces into segments of dirt,cobblestone,or asphalt roads.Moreover,the proposed model significantly outperforms DL networks(CNN,LSTM,and CNN-LSTM).The proposed RE-ResNet achieved the classification accuracies of asphalt roads at 98.98,cobblestone roads at 97.02,and dirt roads at 99.56%,respectively.

Classification of vegetative types in Changbai Mountain based on optical and microwave remote sensing data

Highly accurate vegetative type distribution information is of great significance for forestry resource monitoring and management.In order to improve the classification accuracy of forest types,Sentinel-1 and 2 data of Changbai Mountain protection development zone were selected,and combined with DEM to construct a multi-featured random forest type classification model incorporating fusing intensity,texture,spectral,vegetation index and topography information and using random forest Gini index(GI)for optimization.The overall accuracy of classification was 94.60%and the Kappa coefficient was 0.933.Comparing the classification results before and after feature optimization,it shows that feature optimization has a greater impact on the classification accuracy.Comparing the classification results of random forest,maximum likelihood method and CART decision tree under the same conditions,it shows that the random forest has a higher performance and can be applied to forestry research work such as forest resource survey and monitoring.

Classification of Precipitation Types Using Fall Velocity–Diameter Relationships from 2D-Video Distrometer Measurements

Fall velocity–diameter relationships for four different snowflake types（dendrite,plate,needle,and graupel） were investigated in northeastern South Korea,and a new algorithm for classifying hydrometeors is proposed for distrometric measurements based on the new relationships.Falling ice crystals（approximately 40 000 particles） were measured with a two-dimensional video disdrometer（2DVD） during a winter experiment from 15 January to 9 April 2010.The fall velocity–diameter relationships were derived for the four types of snowflakes based on manual classification by experts using snow photos and 2DVD measurements：the coefficients（exponents） for different snowflake types were 0.82（0.24） for dendrite,0.74（0.35） for plate,1.03（0.71） for needle,and 1.30（0.94） for graupel,respectively.These new relationships established in the present study（PS） were compared with those from two previous studies.Hydrometeor types were classified with the derived fall velocity–diameter relationships,and the classification algorithm was evaluated using 3 × 3 contingency tables for one rain–snow transition event and three snowfall events.The algorithm showed good performance for the transition event：the critical success indices（CSIs） were 0.89,0.61 and 0.71 for snow,wet-snow and rain,respectively.For snow events,the algorithm performance for dendrite and plate（CSIs = 1.0 and 1.0,respectively） was better than for needle and graupel（CSIs = 0.67 and 0.50,respectively）.

Performance-weighted-voting model:An ensemble machine learning method for cancer type classification using whole-exome sequencing mutation

Background:With improvements in next-generation DNA sequencing technology,lower cost is needed to collect genetic data.More machine learning techniques can be used to help with cancer analysis and diagnosis.Methods:We developed an ensemble machine learning system named performance-weighted-voting model for cancer type classification in 6,249 samples across 14 cancer types.Our ensemble system consists of five weak classifiers(logistic regression,SVM,random forest,XGBoost and neural networks).We first used cross-validation to get the predicted results for the five classifiers.The weights of the five weak classifiers can be obtained based on their predictive performance by solving linear regression functions.The final predicted probability of the performance-weighted-voting model for a cancer type can be determined by the summation of each classifier's weight multiplied by its predicted probability.Results:Using the somatic mutation count of each gene as the input feature,the overall accuracy of the performance-weighted-voting model reached 71.46%,which was significantly higher than the five weak classifiers and two other ensemble models:the hard-voting model and the soft-voting model.In addition,by analyzing the predictive pattern of the performance-weighted-voting model,we found that in most cancer types,higher tumor mutational burden can improve overall accuracy.Conclusion:This study has important clinical significance for identifying the origin of cancer,especially for those where the primary cannot be determined.In addition,our model presents a good strategy for using ensemble systems for cancer type classification.

Types and Optimization Paths Between Poverty Alleviation Effectiveness and Rural Revitalization:A Case Study of Hunan Province,China

Exploring the synergy types and optimization paths between Poverty Alleviation Effectiveness and Rural Revitalization is necessary for achieving the two centenary goals.Taking poverty alleviation counties in Hunan Province,China as an example,our study proposed an indicator to measure the synergistic development between Poverty Alleviation Effectiveness and Rural Revitalization using the multi-index integrated evaluation method.Then,the coupling types were classified based on both the proposed indicator and regional characteristics.Besides,the corresponding optimization path for each coupling type was proposed to promote the synergistic development of Poverty Alleviation and Rural Revitalization.Results are as follows:1)Lower synergy focused on the southwestern Hunan,while low synergy is widely distributed(such as the west,southwest,northwest,and midland).Moderate synergy is in the midland,such as Huaihua and Chenzhou cities.High synergy is distributed in Yongzhou,Huaihua,Xiangxi cities,etc.Besides,only Hecheng City belongs to the higher synergy.2)This paper proposes corresponding development paths for different development characteristics and main problems from multiple perspectives of the protection system,industrial planning,and rural market.Continuously consolidate and enhance the effectiveness of Poverty Alleviation and Rural Revitalization to achieve coupled and synergistic development of the two systems.Our research results can provide theoretical support for implementing Poverty Alleviation and Rural Revitalization in Hunan Province,China.

Fuel classification and mapping from satellite imagines

This paper summarizes the fuel type systems currently adopted by the fire danger rating systems or fire behavior prediction systems of some countries, such as Canada, the United States, Australia, Greece, and Switzerland. As an example, the Canadian Forest Fire Danger Rating System organizes fuel types into five major groups, with a total of 16 discrete fuel types recognized. In the United States National Fire Danger Rating System, fuel models are divided into four vegetation groups and twenty fire behavior fuel models. The Promethus System （Greece） divides fuels into 7 types, and Australia has adopted only three distinct fuel types： open grasslands, dry eucalyptus forests, and heath/shrublands. Four approaches to mapping fuels are acceptable： field reconnaissance, direct mapping methods, indirect mapping methods, and gradient modeling. Satellite remote-sensing techniques provide an alternative source of obtaining fuel data quickly, since they provide comprehensive spatial coverage and enough temporal resolution to update fuel maps in a more efficient and timely manner than traditional aerial photography or fieldwork. Satellite sensors can also provide digital information that can be easily tied into other spatial databases using Geographic Information System （GIS） analysis, which can be used as input in fire behavior and growth models. Various fuel-mapping methods from satellite remote sensing are discussed in the paper. According to the analysis of the fuel mapping techniques worldwide, this paper suggests that China should first create appropriate fuel types for its fire agencies before embarking on developing a national fire danger rating system to improve the current data situation for it＇s fire management programs.

Classification of Young Females' Body Shape in Jiaodong Area Based on 3D Morphological Characteristics

To improve the classification method of body type, 103 young female college students in Jiaodong area(Shandong, China) were measured by a 3 D body scanning system, and variables of upper body parts were selected and analyzed by SPSS software. According to the indices such as the chest ratio, the chest sagittal diameter ratio, and the shoulder angle, the tested population was quickly clustered into six categories by the classification method of “size feature+shape index+front and back indices”, which were divided into flat chest body, graceful body, breast augmentation body, normal body, convex back body, and flat body. The proportion of various body types and classification rules were obtained. According to the classification rules, 103 samples and 15 new females’ body data were analyzed and verified. Finally, according to the descriptive statistical analysis of upper body-related indicators of young female in this area, the height and the chest circumference were selected as independent variables, regression analysis was carried out on 11 related indicators, and the mapping relationship between height and chest circumference was studied, which provided a mathematical model for the design of fit clothing structure of young females in Jiaodong area.

Hyper-spectral characteristics and classification of farmland soil in northeast of China

The physical and chemical heterogeneities of soils make the soil spectral different and complicated, and it is valuable to increase the accuracy of prediction models for soil organic matter（SOM） based on pre-classification. This experiment was conducted under a controllable environment, and different soil samples from northeast of China were measured using ASD2500 hyperspectral instrument. The results showed that there are different reflectances in different soil types. There are statistically significant correlation between SOM and reflectence at 0.05 and 0.01 levels in 550–850 nm, and all soil types get significant at 0.01 level in 650–750 nm. The results indicated that soil types of the northeast can be divided into three categories： The first category shows relatively flat and low reflectance in the entire band; the second shows that the spectral reflectance curve raises fastest in 460–610 nm band, the sharp increase in the slope, but uneven slope changes; the third category slowly uplifts in the visible band, and its slope in the visible band is obviously higher than the first category. Except for the classification by curve shapes of reflectance, principal component analysis is one more effective method to classify soil types. The first principal component includes 62.13–97.19% of spectral information and it mainly relates to the information in 560–600, 630–690 and 690–760 nm. The second mainly represents spectral information in 1 640–1 740, 2 050–2 120 and 2 200–2 300 nm. The samples with high OM are often in the left, and the others with low OM are in the right of the scatter plot（the first principal component is the horizontal axis and the second is the longitudinal axis）. Soil types in northeast of China can be classified effectively by those two principles; it is also a valuable reference to other soil in other areas.

Historical-Dynamic Integrative Classification of Basinogenesis and Ore-Forming Basins

Interesting classifications of basinogenesis and basins were proposed by many scientists. They classified basinogenesis and basins mainly from a single angle, either from a historical angle or from a dynamic angle . In order to more comprehensively understand them for more effectively guiding prospecting and exploration, the author integrates the two methods of analysis with each other and proposes an integrative classification .According to the historical - dynamic integrative classification,basinogenesis and basins can be.di-vided into three types :oceanic crust type ,embryo-continental (transitional )crust type and continental crust type .Oceanic crust type can be subdivided into mobile region type (mainly tenskmal )and stable region type . Embryo-continental type includes pre-geosynclinal type (divisible into several mobile region types and stable region types with tensional type predominating among mobile region types ) and ear ly-geosynclinal type (mainly tenskmal ) .Continental crust type includes late- geosynclinal (fold belt)type (compressional or tenskmal ),platform type (mainly sinking and rarely tenskmal subsidence-aulacogen)and geodepression (diwa )type (compressional , tenskmal or compresskmal-tenskmal ).

Variability of Raindrop Size Distribution during a Regional Freezing Rain Event in the Jianghan Plain of Central China

The characteristics of the raindrop size distribution(DSD)during regional freezing rain(FR)events that occur throughout the phase change(from liquid to solid)are poorly understood due to limited observations.We investigate the evolution of microphysical parameters and the key formation mechanisms of regional FR using the DSDs from five disdrometer sites in January 2018 in the Jianghan Plain(JHP)of Central China.FR is identified via the size and velocity distribution measured from a disdrometer,the discrete Fréchet distancemethod,surface temperature,human observations,and sounding data.With the persistence of precipitation,the emergence of graupel or snowflakes significantly reduces the proportion of FR.The enhancement of this regional FR event is mainly dominated by the increase in the number concentration of raindrops but weakly affected by the diameters.To improve the accuracy of quantitative precipitation estimation for the FR event,a modified second-degree polynomial relation between the shapeμand slopeΛof gamma DSDs is derived,and a new Z-R(radar reflectivity to rain rate)relationship is developed.The mean values of mass-weighted mean diameters(D_(m))and generalized intercepts(lgN_(w))in FR are close to the stratiform results in the northern region of China.Both the melting of tiny-rimed graupels and large-dry snowflakes are a response to the formation of this regional FR process in the JHP,dominated by the joint influence of the physical mechanism of warm rain,vapor deposition,and aggregation/riming coupled with the effect of weak convective motion in some periods.

Tight sandstone gas accumulation mechanism and development models

Tight sandstone gas serves as an important unconventional hydrocarbon resource, and outstanding results have been obtained through its discovery both in China and abroad given its great resource potential. However, heated debates and gaps still remain regarding classification standards of tight sandstone gas, and critical controlling factors, accumulation mechanisms, and devel- opment modes of tight sandstone reservoirs are not deter- mined. Tight sandstone gas reservoirs in China are generally characterized by tight strata, widespread distri- bution areas, coal strata supplying gas, complex gas-water relations, and abnormally low gas reservoir pressure. Water and gas reversal patterns have been detected via glass tube and quartz sand modeling, and the presence of critical geological conditions without buoyancy-driven mecha- nisms can thus be assumed. According to the timing of gas charging and reservoir tightening phases, the following three tight sandstone gas reservoir types have been identified： （a） ＂accumulation-densification＂ （AD）, or the conventional tight type, （b） ＂densification-accumulation＂ （DA）, or the deep tight type, and （c） the composite tight type. For the AD type, gas charging occurs prior to reser- voir densification, accumulating in higher positions under buoyancy-controlled mechanisms with critical controlling factors such as source kitchens （S）, regional overlaying cap rocks （C）, gas reservoirs, （D） and low fluid potential areas （P）. For the DA type, reservoir densification prior to the gas charging period （GCP） leads to accumulation in depres- sions and slopes largely due to hydrocarbon expansive forces without buoyancy, and critical controlling factors are effective source rocks （S）, widely distributed reservoirs （D）, stable tectonic settings （W） and universal densification of reservoirs （L）. The composite type includes features of the AD type and DA type, and before and after reservoir densification period （RDP）, gas charging and accumulation is controlled by early buoyancy and later molecular expansive force respectively. It is widely distributed in anticlinal zones, deep sag areas and slopes, and is con- trolled by source kitchens （S）, reservoirs （D）, cap rocks （C）, stable tectonic settings （W）, low fluid potential areas （P）, and universal reservoir densification （L）. Tight gas resources with great resource potential are widely dis- tributed worldwide, and tight gas in China that presents advantageous reservoir-forming conditions is primarily found in the Ordos, Sichuan, Tarim, Junggar, and Turpan- Hami basins of central-western China. Tight gas has served as the primary impetus for global unconventional natural gas exploration and production under existing technical conditions.

A New Fragile Watermarking Scheme for Text Documents Authentication

Because there are different modification types of deleting characters and inserting characters in text documents, the algorithms for image authentication can not be used for text documents authentication directly. A text watermarking scheme for text document authentication is proposed in this paper. By extracting the features of character cascade together with the user secret key, the scheme combines the features of the text with the user information as a watermark which is embedded into the transformed text itself. The receivers can verify the integrity and the authentication of the text through the blind detection technique. A further research demonstrates that it can also localize the tamper, classify the type of modification, and recover part of modified text documents. The aforementioned conclusion has been proved by both our experiment results and analysis.

Distinct Aeolian-fluvial Interbedded Landscapes in Three Watersheds of the Northern China

Due to the complex dynamic of aeolian and fluvial interacted processes behind the landform development,most of previous works started from classifying the types of landscape characterized by various aeolian and fluvial features.Such classifications are usually generalized based on large geomorphic data set abstracted from satellite images without field verification and dynamic field data.In this study,we identified river banks in deserts as a unique geographical unit dominated by aeolian-fluvial processes.Three distinct locations have been identified as representative study cases,which are in the Keriya River Basin in the west,the Mu Bulag River Basin in the middle and the Xar Moron River Basin in the east of the northern China.The aeolian-fluvial interaction types were quantified based on site observation and measurement,topographic mapping and remote-sensing image analysis.Dimensional morphological relationship between river channel and adjacent sand dunes areas were explored.We concluded that different channels are often associated with different distributions of riparian dunes.The quantitative data enabled us to distinguish statistically four different types of landscape in aeolian-fluvial dominant environment,namely riverside dunes-straight channel,symmetrical interleaving dunes-meandering channel,river-island dunes-braiding channel,and grid-like dunes-anastomosing channel,aiming to provide compensational information to current aeolian-fluvial interaction studies.The angle of interaction between aeolian and fluvial systems,the windward and leeward sites of the bank,vegetation coverage and underlying landform determines the distribution,morphology,scale and direction of extension of the riparian dunes.The results of the work study can provide a reference for study of aeolian-fluvial interactions at different spatial scales in arid region.

Influence of the Boundary Forcing on the Internal Variability of a Regional Climate Model

The internal variability of a ten-member ensemble of the regional climate model REMO over Europe is investigated. It is shown that the annual cycle of internal variability behaves differently compared to earlier studies that focused on other regions. To gain better insight into the dependence of the internal variability on the boundary forcing variability, a circulation type classification is performed on the forcing data. It can be shown that especially in the winter season internal variability is dependent on the circulation type included in the boundary forcing, whereas in the summer season the level and pattern of internal variability is rather independent from the circulation type of the driving field. It is concluded that for Europe the internal variability of REMO in winter is governed by circulation patterns related to the North-Atlantic Oscillation, whereas in summer local processes play a bigger role.

Crop type mapping using LiDAR,Sentinel-2 and aerial imagery with machine learning algorithms

LiDAR data are becoming increasingly available,which has opened up many new applications.One such application is crop type mapping.Accurate crop type maps are critical for monitoring water use,estimating harvests and in precision agriculture.The traditional approach to obtaining maps of cultivated fields is by manually digitizing the fields from satellite or aerial imagery and then assigning crop type labels to each field-often informed by data collected during ground and aerial surveys.However,manual digitizing and labeling is time-consuming,expensive and subject to human error.Automated remote sensing methods is a cost-effective alternative,with machine learning gaining popularity for classifying crop types.This study evaluated the use of LiDAR data,Sentinel-2 imagery,aerial imagery and machine learning for differentiating five crop types in an intensively cultivated area.Different combinations of the three datasets were evaluated along with ten machine learning.The classification results were interpreted by comparing overall accuracies,kappa,standard deviation and f-score.It was found that LiDAR data successfully differentiated between different crop types,with XGBoost providing the highest overall accuracy of 87.8%.Furthermore,the crop type maps produced using the LiDAR data were in general agreement with those obtained by using Sentinel-2 data,with LiDAR obtaining a mean overall accuracy of 84.3%and Sentinel-2 a mean overall accuracy of 83.6%.However,the combination of all three datasets proved to be the most effective at differentiating between the crop types,with RF providing the highest overall accuracy of 94.4%.These findings provide a foundation for selecting the appropriate combination of remotely sensed data sources and machine learning algorithms for operational crop type mapping.

Phenological metrics-based crop classification using HJ-1 CCD images and Landsat 8 imagery

Crop type data are an important piece of information for many applications in agriculture.Extracting crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limited availability of satellite images due to weather conditions.In this research,we aim at producing crop maps for areas with abundant rainfall and small-sized parcels by making full use of Landsat 8 and HJ-1 charge-coupled device(CCD)data.We masked out non-vegetation areas by using Landsat 8 images and then extracted a crop map from a longterm time-series of HJ-1 CCD satellite images acquired at 30-m spatial resolution and two-day temporal resolution.To increase accuracy,four key phenological metrics of crops were extracted from time-series Normalized Difference Vegetation Index curves plotted from the HJ-1 CCD images.These phenological metrics were used to further identify each of the crop types with less,but easier to access,ancillary field survey data.We used crop area data from the Jingzhou statistical yearbook and 5.8-m spatial resolution ZY-3 satellite images to perform an accuracy assessment.The results show that our classification accuracy was 92%when compared with the highly accurate but limited ZY-3 images and matched up to 80%to the statistical crop areas.